基于非平稳系统辨识的心音包络自适应分割

doi:10.19678/j.issn.1000-3428.0055189

计算机工程 ›› 2020, Vol. 46 ›› Issue (8): 290-296,304. doi: 10.19678/j.issn.1000-3428.0055189

基于非平稳系统辨识的心音包络自适应分割

许春冬¹, 周静¹, 应冬文^1,2, 龙清华¹

1. 江西理工大学信息工程学院, 江西赣州 341000;
2. 中国科学院声学研究所语言声学与内容理解重点实验室, 北京 100190

收稿日期:2019-06-12 修回日期:2019-08-08 发布日期:2019-08-14
作者简介:许春冬(1976-),男,副教授、博士,主研方向为心音信号处理;周静,硕士研究生;应冬文,研究员、博士;龙清华,硕士研究生。
基金资助:
国家自然科学基金（11864016，11704164）；国家级大学生创新创业训练计划项目（201810407019）；江西省文化艺术科学规划项目（YG2017384）；江西省研究生创新项目（YC2018-S330）。

Adaptive Envelope Segmentation of Heart Sound Based on Non-Stationary System Identification

XU Chundong¹, ZHOU Jing¹, YING Dongwen^1,2, LONG Qinghua¹

1. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China;
2. Key Laboratory of Language Acoustics and Content Understanding, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China

Received:2019-06-12 Revised:2019-08-08 Published:2019-08-14

摘要/Abstract

摘要： 为实现心音信号的有效分割，提出一种基于非平稳系统辨识的心音信号特征包络自适应分割方法。根据非平稳系统辨识原理，提取心音信号特征包络并对包络作平滑与展宽处理。基于重尺度小波降噪信噪比与特征包络均值参数，创建自适应阈值函数进行心音信号分割，同时利用包络与时域特征剔除因噪音及杂音引起的错误分割点。实验结果表明，该方法能够有效提取基础心音信号特征，分割精度达到89.21%，相比维奥拉积分包络分割法、改进型希尔伯特-黄变换包络双阈值分割法等对比方法分割精度更高、实时性更强。

关键词: 非平稳系统辨识, 心音信号, 心音分割, 自适应阈值, 包络提取

Abstract: To effectively segment Heart Sound Signal(HSS),this paper proposes an adaptive segmentation method for heart sound based on Non-Stationary System Identification(NSSI),so as to extract the envelope of HSS,and smooth and broaden it.The method uses the noise reduction Signal to Noise Rate(SNR) of heavy-scale wavelet and the mean parameter of feature envelope to establish the adaptive threshold function for signal segmentation of heart sound.Also,the false segmentation points caused by noise and murmur are excluded according to the envelope and time domain features.Experimental results show that the proposed method can effectively extract the basic features of HSS,increasing the segmentation accuracy to 89.21%.Compared with the envelope segmentation method of Viola integral,improved double-threshold envelope segmentation method based on Hilbert-Huang transform and other methods,the proposed method has higher segmentation accuracy and real-time performance.

Key words: Non-Stationary System Identification(NSSI), Heart Sound Signal(HSS), heart sound segmentation, adaptive threshold, envelope extraction

中图分类号:

TN912

许春冬, 周静, 应冬文, 龙清华. 基于非平稳系统辨识的心音包络自适应分割[J]. 计算机工程, 2020, 46(8): 290-296,304.

XU Chundong, ZHOU Jing, YING Dongwen, LONG Qinghua. Adaptive Envelope Segmentation of Heart Sound Based on Non-Stationary System Identification[J]. Computer Engineering, 2020, 46(8): 290-296,304.

http://www.ecice06.com/CN/Y2020/V46/I8/290

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参考文献

[1] WANG H,ABAJOBIR A,ABATE K H,et al.Global,regional,and national under-5 mortality,adult mortality,age-specific mortality,and life expectancy,1970-2016:asystematic analysis for the global burden of disease study2016[J].The Lancet,2017,390(10100):1084-1150.
[2] JAIN P K,TIWARI A K.Heart monitoring systems-a review[J].Computers in Biology and Medicine,2014,54(C):1-13.
[3] CLIFFORD G D,LIU C,MOODY B,et al.Classification of normal/abnormal heart sound recordings:the physionet/computing in cardiology challenge2016[C]//Proceedings of2016Computing in Cardiology Conference.Washington D.C.,USA:IEEE Press,2016:1-8.
[4] HOU Leijing,GUO Tingting,SUN Yan,et al.A personalized Gaussian mixture model modeling method for heart sound segmentation[J].Acta Acustica,2019,44(1):20-27.(in Chinese) 侯雷静,郭婷婷,孙燕,等.面向心音分割的个性化高斯混合建模方法[J].声学学报,2019,44(1):20-27.
[5] GUO Xingming,JIANG Hong,ZHENG Yineng.Heart sound signal envelope extraction based on modified Viola integral method[J].Chinese Journal of Scientific Instrument,2016,37(10):2352-2358.(in Chinese) 郭兴明,蒋鸿,郑伊能.基于改进的维奥拉积分方法提取心音信号包络[J].仪器仪表学报,2016,37(10):2352-2358.
[6] REN Haoran,JIN Hailong,CHEN Chen,et al.A novel cardiac auscultation monitoring system based on wireless sensing for healthcare[J].IEEE Journal of Biomedical and Health Informatics,2018,6:1-13.
[7] CHEN Jie,HOU Hailiang,LUO Liangcai,et al.Automatic identification method for the first and second heart sound based on double-threshold[J].Computer Engineering,2012,38(16):174-177,181.(in Chinese) 陈洁,侯海良,罗良才,等.基于双门限的第一、第二心音自动识别方法[J].计算机工程,2012,38(16):174-177,181.
[8] WANG Ouyang,ZHEN Hui,YAO Jian.Fetal heart rate detection system based on smartphone[J].Computer Engineering,2016,42(4):288-294.(in Chinese) 王欧阳,甄辉,姚剑.基于智能手机的胎心率检测系统[J].计算机工程,2016,42(4):288-294.
[9] ZHAO Zhan,ZHANG Xuru,FANG Zhen,et al.Phonocardio-gram segmentation and abnormal phonocardiogram screening algorithm based on cardiac cycle estimation[J].Journal of Electronics and Information Technology,2017,39(11):2677-2683.(in Chinese) 赵湛,张旭茹,方震,等.基于心动周期估计的心音分割及异常心音筛查算法[J].电子与信息学报,2017,39(11):2677-2683.
[10] BELMECHERI M Z,SHFIR M,KALE I.Automatic heart sounds segmentation based on the correlation coefficients matrix for similar cardiac cycles identification[J].Biomedical Signal Processing and Control,2018,43:300-310.
[11] XU Chundong,ZHOU Jing,YING Dongwen,et al.Optimization of the extraction method of MFCC feature vectors for heart sound signals[J].Signal Processing,2019,35(3):410-418.(in Chinese) 许春冬,周静,应冬文,等.心音信号MFCC特征向量提取方法的优化[J].信号处理,2019,35(3):410-418.
[12] XU Chundong,ZHOU Jing,LONG Qinghua.et al.A adaptive threshold method for heart sound de-noising based on coif-5 wavelet[J].Science Technology and Engineering,2019,19(2):106-113.(in Chinese) 许春冬,周静,龙清华,等.基于coif-5小波的心音自适应阈值降噪方法[J].科学技术与工程,2019,19(2):106-113.
[13] GHAZAL A,YUAN Y,WANG C X,et al.A non-stationary IMT-advanced MIMO channel model for high-mobility wireless communication systems[J].IEEE Transactions on Wireless Communications,2017,16(4):2057-2068.
[14] GANNOT S,BURSHTEIN D,WEINSTEIN E.Signal enhancement using beamforming and nonstationarity with applications to speech[J].IEEE Transactions on Signal Processing,2001,49(8):1614-1626.
[15] SHALVI O,WEINSTEIN E.System identification using non-stationary signals[J].IEEE Transactions on Signal Processing,1996,44(8):2055-2063.
[16] CHENG Xiefeng,SHE Chenjun,LI Ji.Anti-control method of heart sound based on its chaotic characteristics[J].Journal of Vibration and Shock,2018,37(17):178-184.(in Chinese)成谢锋,佘辰俊,李吉.基于混沌特性的心音反控制方法研究[J].振动与冲击,2018,37(17):178-184.
[17] SHAMSI H,OZBEK I Y.Heart sound localization in chest sound using temporal fuzzy c-means classification[C]//Proceedings of 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.Washington D.C.,USA:IEEE Press,2012:5286-5289.
[18] JAIN P K,TIWARI A K.An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal[J].Biomedical Signal Processing and Control,2017,38:388-399.
[19] LI Shuang.Heart sound signal recognition and classification based on envelope extraction[D].Kaifeng:Henan University,2018.(in Chinese)李爽.基于包络提取的心音信号识别与分类[D].开封:河南大学,2018.
[20] LEE Y,LEE G,NA S D,et al.Heart murmur detection algorithm based on spectral flatness[J].Journal of Korea Multimedia Society,2016,19(3):557-566.
[21] SHARMA L N.Multiscale analysis of heart sound for segmentation using multiscale Hilbert envelope[C]//Proceedings of International Conference on ICT and Knowledge Engineering.Washington D.C.,USA:IEEE Press,2016:33-37.
[22] SUN Shuping,JIANG Zhongwei,WANG Haibin,et al.Automatic moment segmentation and peak detection analysis of heart sound pattern via short-time modified Hilbert transform[J].Computer Methods and Programs in Biomedicine,2014,114(3):219-230.

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